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Found 5 papers, 0 papers with code
Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework
We modeled electroencephalography (EEG) responses to acoustic, segmentation, and linguistic speech representations of a story using convolutional neural networks trained on a large sample of healthy participants, serving as a model for intact neural tracking of speech.
The role of vowel and consonant onsets in neural tracking of natural speech
Our results show that vowel-consonant onsets outperform onsets of any phone in both tasks, which suggests that neural tracking of the vowel vs. consonant exists in the EEG to some degree.
Detecting post-stroke aphasia using EEG-based neural envelope tracking of natural speech
In this study, we aimed to test the potential of the neural envelope tracking technique for detecting language impairments in individuals with aphasia (IWA).
Relating EEG to continuous speech using deep neural networks: a review
Linear models are presently used to relate the EEG recording to the corresponding speech signal.
Relating the fundamental frequency of speech with EEG using a dilated convolutional network
To investigate how speech is processed in the brain, we can model the relation between features of a natural speech signal and the corresponding recorded electroencephalogram (EEG).
